Challenge: Simulation models allows us to run many different replicates to test the uncertainty in results.
For example, we can simulate a harvest strategy using different samples for population parameters like productivity and capacity.
A common approach is to estimate probability distributions for each parameter, and then run the model with many random samples.
Imagine a model with 10 stocks running over 48 simulated years for each of 500 parameter samples. The output for each
scenario is 5000 trajectories and a total of almost a 1/4 Million data points. This amount of data can be summarized
using many different performance measures (e.g. overall median value, lower quartile over first 12 years), but
the pattern in trajectories tends to get lost.

Solution: Capture the patterns in trajectories in a 3-step approach: (1) calculate slopes for
a moving time window, say 12 years, and compare them to some critical value, like declining more than 15% (2)
plot a heatmap to show declining vs. stable/increasing by year (3) summarize the heatmap into a single
line based on the proportion stable/increasing. This can extract a very strong underlying signal out of the 500
trajectories.